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首页> 外文期刊>Expert Systems with Application >Two-Tier genetic programming: towards raw pixel-based image classification
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Two-Tier genetic programming: towards raw pixel-based image classification

机译：两层遗传程序设计：基于原始像素的图像分类

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摘要

Classifying images is of great importance in machine vision and image analysis applications such as object recognition and face detection. Conventional methods build classifiers based on certain types of image features instead of raw pixels because the dimensionality of raw inputs is often too large. Determining an optimal set of features for a particular task is usually the focus of conventional image classification methods. In this study we propose a Genetic Programming (GP) method by which raw images can be directly fed as the classification inputs. It is named as Two-Tier GP as every classifier evolved by it has two tiers, the other for computing features based on raw pixel input, one for making decisions. Relevant features are expected to be self-constructed by GP along the evolutionary process. This method is compared with feature based image classification by GP and another GP method which also aims to automatically extract image features. Four different classification tasks are used in the comparison, and the results show that the highest accuracies are achieved by Two-Tier GP. Further analysis on the evolved solutions reveals that there are genuine features formulated by the evolved solutions which can classify target images accurately.
机译：对图像进行分类在机器视觉和图像分析应用（例如目标识别和面部检测）中非常重要。传统方法基于某些类型的图像特征而不是原始像素来构建分类器，因为原始输入的维数通常太大。确定特定任务的最佳功能集通常是常规图像分类方法的重点。在这项研究中，我们提出了一种遗传规划（GP）方法，通过该方法可以将原始图像直接作为分类输入。它被命名为“两层GP”，因为它演变出的每个分类器都有两层，另一层用于基于原始像素输入来计算功能，一个用于决策。 GP预期在进化过程中会自行构建相关功能。将该方法与GP和另一种旨在自动提取图像特征的GP方法与基于特征的图像分类进行了比较。比较中使用了四个不同的分类任务，结果表明，双层GP实现了最高的准确性。对演化解决方案的进一步分析表明，演化解决方案具有真实的功能，可以对目标图像进行准确分类。

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  • 来源
    《Expert Systems with Application》 |2012年第16期|p.12291-12301|共11页
  • 作者

    Harith Al-Sahaf; Andy Song; Kourosh Neshatian; Mengjie Zhang;

  • 作者单位

    School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington 6140, New Zealand;

    School of Computer Science and Information Technology, RMIT University, C.P.O. Box 2476, Melbourne 3001, Australia;

    School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington 6140, New Zealand,Department of Computer Science and Software Engineering, University of Canterbury, Private bag 4800, Christchurch 8140, New Zealand;

    School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington 6140, New Zealand;

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  • 正文语种 eng
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  • 关键词

    evolutionary computation; genetic programming; feature extraction; feature selection; image classification;

    机译：进化计算基因编程;特征提取;特征选择;图像分类;

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